class TuneHyperparameters extends Estimator[TuneHyperparametersModel] with Wrappable with ComplexParamsWritable with HasEvaluationMetric with SynapseMLLogging

Tunes model hyperparameters

Allows user to specify multiple untrained models to tune using various search strategies. Currently supports cross validation with random grid search.

Linear Supertypes
SynapseMLLogging, HasEvaluationMetric, ComplexParamsWritable, MLWritable, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, Estimator[TuneHyperparametersModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. TuneHyperparameters
  2. SynapseMLLogging
  3. HasEvaluationMetric
  4. ComplexParamsWritable
  5. MLWritable
  6. Wrappable
  7. DotnetWrappable
  8. RWrappable
  9. PythonWrappable
  10. BaseWrappable
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new TuneHyperparameters()
  2. new TuneHyperparameters(uid: String)

Value Members

  1. final def clear(param: Param[_]): TuneHyperparameters.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): Estimator[TuneHyperparametersModel]
    Definition Classes
    TuneHyperparameters → Estimator → PipelineStage → Params
  3. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  4. val evaluationMetric: Param[String]
    Definition Classes
    HasEvaluationMetric
  5. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  6. def explainParams(): String
    Definition Classes
    Params
  7. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  8. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  9. def fit(dataset: Dataset[_]): TuneHyperparametersModel

    Tunes model hyperparameters for given number of runs and returns the best model found based on evaluation metric.

    Tunes model hyperparameters for given number of runs and returns the best model found based on evaluation metric.

    dataset

    The input dataset to train.

    returns

    The trained classification model.

    Definition Classes
    TuneHyperparameters → Estimator
  10. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[TuneHyperparametersModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  11. def fit(dataset: Dataset[_], paramMap: ParamMap): TuneHyperparametersModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  12. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TuneHyperparametersModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  13. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  14. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  15. def getEvaluationMetric: String

    Definition Classes
    HasEvaluationMetric
  16. def getModels: Array[Estimator[_]]

  17. def getNumFolds: Int

  18. def getNumRuns: Int

  19. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  20. def getParallelism: Int

  21. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  22. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  23. def getParamSpace: ParamSpace

  24. def getSeed: Long

  25. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  26. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  27. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  28. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  29. def logClass(): Unit
    Definition Classes
    SynapseMLLogging
  30. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  31. def logTrain[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  32. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  33. def logVerb[T](verb: String, f: ⇒ T, columns: Int = -1): T
    Definition Classes
    SynapseMLLogging
  34. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  35. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  36. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  37. val models: EstimatorArrayParam

    Estimators to run

  38. val numFolds: IntParam
  39. val numRuns: IntParam
  40. val parallelism: IntParam
  41. val paramSpace: ParamSpaceParam
  42. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  43. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  44. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  45. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  46. val seed: LongParam
  47. final def set[T](param: Param[T], value: T): TuneHyperparameters.this.type
    Definition Classes
    Params
  48. def setEvaluationMetric(value: String): TuneHyperparameters.this.type

    Definition Classes
    HasEvaluationMetric
  49. def setModels(value: ArrayList[Estimator[_]]): TuneHyperparameters.this.type
  50. def setModels(value: Array[Estimator[_]]): TuneHyperparameters.this.type

  51. def setNumFolds(value: Int): TuneHyperparameters.this.type

  52. def setNumRuns(value: Int): TuneHyperparameters.this.type

  53. def setParallelism(value: Int): TuneHyperparameters.this.type

  54. def setParamSpace(value: ParamSpace): TuneHyperparameters.this.type

  55. def setSeed(value: Long): TuneHyperparameters.this.type

  56. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  57. def transformSchema(schema: StructType): StructType
    Definition Classes
    TuneHyperparameters → PipelineStage
    Annotations
    @DeveloperApi()
  58. val uid: String
    Definition Classes
    TuneHyperparameters → SynapseMLLogging → Identifiable
  59. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable